Ground Penetrating Radar Antenna Research Articles

A low-profile ultra-wideband magneto-electric dipole antenna for ground penetrating radar

ABSTRACT Ultra-wideband antennas that operate at low frequencies usually have a large geometry, which makes them very limited in practical engineering application environment where space is often constrained. In this paper, a novel low-frequency low-profile ultra-wideband magneto-electric (ME) dipole antenna is designed for the application in ground penetrating radar (GPR) to detect deeply buried targets. The antenna is designed at the operation frequency of 230 MHz, so that the objects that are buried as deep as 1.5 m can be detected. Compared to the traditional low-frequency _antennas, it has a profile as low as 740 mm × 140 mm × 150 mm ( 0.49 λ × 0.09 λ × 0.10 λ ). Based on the simulation analysis and validation measurements, the proposed ME dipole antenna has a wide impedance bandwidth for S 11 < − 10 dB from 135 MHz to 382 MHz even when environmental influence is considered. The antenna is further validated with a GPR experiment, where a pipeline with a diameter of about 200 mm at 1200 mm beneath the road surface is successfully detected by an impulse GPR system where the proposed antennas are integrated.

Ground-penetrating radar scanning and historical interpretation of the location of the destroyed Epiphany Cathedral in Kyiv Brotherhood Monastery (Ukraine)

Background The article presents results of a ground penetrating radar (GPR) scanning carried out in a site of the Epiphany Cathedral of Brotherhood Monastery in Kyiv, Ukraine, destroyed in 1936 by the Soviets. The Cathedral is known as a burial site of Hetman Petro Sahaidachnyi (1582–1622), a Ukrainian Cossack political and civic leader, guardian and patron of Kyiv Brotherhood Monastery. The collection of archival documents, blueprints, stock sources, photographs and cartographic materials of the 17th-19th centuries, as well as historical works of the 19th-21st centuries, were thoroughly analyzed and used as a basis for the interpretation of geophysical results. The set of historical data covers the period from 1615, that is, from the beginning of the construction of the wooden Epiphany Church, the predecessor of the cathedral, to the present day. Methods Summarized information about the construction, restoration after the fire, functioning and destruction of the Cathedral, as well as about the construction on its site in the 20th century, archaeological research in the 20th-21st centuries, was used to clarify possible location of hidden foundations and target GPR measurements. In this context, written testimonies of archaeologists who personally observed the foundations of the cathedral became especially valuable. The shielded GPR antennas, with a central frequency of 300 MHz and 700 MHz, is used for non-invasive investigation. Results GPR scanning specified the site and showed the best-preserved group of foundations of the western wall of the Epiphany Cathedral. Conclusions An integrated historical and geophysical study provided the basis to certify the foundation of the Epiphany Cathedral as a protected object of cultural heritage and facilitated its archaeological research.

Accuracy analysis in determining the location of underground objects using GPR involving lidar data

Ground penetrating radar (GPR) is one of the most useful non-destructive techniques for locating underground objects. Advancements in this technology have facilitated the development of new sensors over the past decade. In this paper, an accuracy assessment of the location of underground objects using various GPR antennas is presented. To achieve the stated goals, measurements of 5 concrete slabs, reinforced with steel bars of various diameters and located at variable depths were taken. The experiment includes the usage of three GPR antennas to assess the format, characteristics, and differences of extracted data. This set of antennas from different manufacturers varied in terms of operating frequency. Additional lidar data from TLS (terrestrial laser scanning) was utilized in the methodology to provide precise surface measurements and therefore, external orientation of the surveyed data. The experiment allowed for the determination of vertical and horizontal accuracy for three tested antennas and the assessment of increasing errors value with greater depth of the measured items, which is important for surveying accuracy forecasting.

3D ground-penetrating radar data analysis and interpretation using attributes based on the gradient structure tensor

In near-surface geophysics, ground-penetrating radar (GPR) surveys are routinely used in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. Thanks to recent technical developments in GPR instrumentation and antenna design, 3D surveys comprising several hundred thousand traces can be performed daily. Especially in complex environments such as sedimentary systems, analyzing and interpreting the resulting GPR volumes is a time-consuming and laborious task that is still largely performed manually. In the past few decades, several data attributes have been developed to guide and improve such tasks and assure a higher degree of reproducibility in the resulting interpretations. Many of these attributes have been developed in image processing or computer vision and are routinely used, for example, in reflection seismic data interpretation. Especially in sedimentary systems, variations in the subsurface are accompanied by variations of GPR reflections in terms of the amplitudes, continuity, and geometry in view of the dip angle and direction. A promising tool to analyze such structural features is known as the gradient structure tensor (GST). To date, the application of the GST approach has been limited to a few 2D GPR examples. Thus, we take the basic idea of GST analysis and introduce and evaluate the corresponding attributes to analyze 3D GPR data. We apply our GST approach to one synthetic and two field data sets imaging diverse sedimentary structures. Our results demonstrate that our set of GST-based attributes can be efficiently computed in three dimensions and that these attributes represent versatile measures to address different typical interpretation tasks and, thus, help for an efficient, reproducible, and more objective interpretation of 3D GPR data.

Applying Ground Penetrating Radar and Electrical Resistivity Tomography for the Detection of Archaeological Structures in a Pre‐Tarascan Classic‐Epiclassic Site, Tingambato, Michoacán, Mexico

ABSTRACTA survey was conducted to investigate buried archaeological remains at Tingambato, a pre‐Tarascan classic‐epiclassic archaeological site located in the north‐central part of the State of Michoacán in western Mexico, using ground‐penetrating radar (GPR) and electrical resistivity tomography (ERT). The aim of this study was to detect the foundations (ancient buried walls) and cavities (tombs), define the geometry of the foundations and correlate construction style and depth with relative chronological buried structures. The survey was carried out on two grids of 15 m × 37 m (Zona Verde) and 10.25 m × 36.5 m (Ballgame court), using a 200 MHz antenna for GPR and Schlumberger‐Wenner arrays for ERT. GPR 3D acquisition was carried out along parallel lines spaced 0.25 m apart in a single direction. In the first area, considering the geometric shape found at a depth of 1.35 m, we can assume the existence of a buried structure, probably wall remains. In the second area, a very diffractive zone coincides with a resistive anomaly (&gt; 2000 ohm·m). In order to refine the GPR results, synthetic modelling and a comparison with real traces were carried out. The 1D GPR modelling allows us to precise the presence of a cavity with a rectangular cross section. Since the type of volcaniclastic avalanche deposits of the subsoil do not allow the formation of cavities of that size, we infer that it may be the remains of a tomb or an anthropogenic cavity.

Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.

Automatic Object Detection in Radargrams of Multi-Antenna GPR Systems Based on Simulation Data for Railway Infrastructure Analysis

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations.

Increasing the probing depth of pulsed GPR by reducing the antenna impedance

At the present time, dipole antennas with resistively loaded vibrators are widely used in pulsed georadar. The main property of such antennas is the emission of an electromagnetic wave into the subsurface medium with rapid attenuation of the ultra-wideband pulse to the end of the vibrators without reflection. When the total resistance of the vibrator decreases below 200 Ohms, parasitic self-oscillations appear in this antenna, limiting the increase in power supplied to the antenna. Development of a dipole antenna for subsurface ground penetrating radar with the ability to reduce the antenna impedance below 200 Ohms to increase the radiated power and increase the depth of ground penetrating radar. The design of a dipole antenna with several resistively loaded vibrators connected in parallel is proposed. The total resistance of a single vibrator is selected based on the stability requirements to prevent parasitic self-oscillations, but the impedance of the dipole antenna decreases in proportion to the number of connected vibrators. A dipole antenna with five dipoles connected and arranged parallel to each other has a gain 10 dB higher compared to a dipole with a single resistively loaded dipole. Compared to a plate antenna 3 meters long and 25 cm wide, a five-vibrator dipole antenna of the same length has a gain of 3.3 dB higher. Another advantage of the proposed antenna is its higher resolution. At the same time, insulated vibrators made of flexible conductors allow twisting to reduce the overall diameter of the dipole antenna, which allows its use in difficult engineering and geological conditions. Experiments with three-meter dipole antennas showed an increase in sounding depth in the Moscow region from 14 to 20 meters.

A convolutional neural network for creating near-surface 2D velocity images from GPR antenna measurements

Abstract In this research, our focus lies in exploring the effectiveness of a frequency-velocity convolutional neural network (CNN) in the efficient and non-intrusive acquisition of 2D wave velocity visuals of near-surface geological substances, accomplished through the analysis of data from ground-penetrating radar (GPR). To learn complex correlations between antenna readings and subsurface velocities, the proposed CNN model makes use of the spatial features present in the GPR data. By employing a network architecture capable of accurately detecting both local and global patterns within the data, it becomes feasible to efficiently extract valuable velocity information from GPR readings. The CNN model is trained and validated using a substantial dataset consisting of GPR readings along with corresponding ground truth velocity images. Diverse subsurface settings, encompassing different soil types and geological characteristics, are employed to gather the GPR measurements. In the supervised learning approach employed to train the CNN model, the GPR measurements serve as input, while the associated ground truth velocity images are utilized as target outputs. The model is trained using backpropagation and optimized using a suitable loss function to reduce the difference between the predicted velocity images and the actual images. The experimental results demonstrate the effectiveness of the proposed CNN method in accurately deriving 2D velocity images of near-surface materials from GPR antenna observations. Compared to traditional techniques, the CNN model exhibits superior velocity calculation precision and achieves high levels of accuracy. Moreover, when applied to unseen GPR data, the trained model exhibits promising generalization abilities, highlighting its potential for practical subsurface imaging applications.

Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm.

The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.

Design and Realization of 2.4 GHz Bowtie Antenna for Ground Penetrating Radar (GPR)

Design and Realization of 2.4 GHz Bowtie Antenna for Ground Penetrating Radar (GPR)

GPR method for estimation of unbound load-bearing layers’ parameters using machine learning and interactive modelling

The demand for nondestructive testing techniques (NDTs) that can be implemented continuously and cost-effectively for large-scale study purposes is increasing. Ground penetrating radar (GPR) as an NDT method permits the estimation of pavement materials characteristics without disrupting the serviceability of the system. This research used typical pavement materials for constructing load-bearing layers (base and subbase) for the GPR laboratory tests. A 2 GHz GPR antenna was applied to execute the tests by changing three essential variables of the material: water content, compaction and clay content. Machine learning and interactive methods were innovatively utilised to model the collected data. As a result, a multivariate non-linear empirical function is proposed by the interactive procedure. Furthermore, the machine learning modelling (SVM method) with R2 of 0.91 indicates promising results to evaluate the pavement layers’ properties. Machine learning can enhance the speed and accuracy of analysis when faced with multi-variables and extensive data.

Ground-penetrating radar scanning and historical interpretation of the location of the destroyed Epiphany Cathedral in Kyiv Brotherhood Monastery (Ukraine).

The article presents results of a ground penetrating radar (GPR) scanning carried out in a site of the Epiphany Cathedral of Brotherhood Monastery in Kyiv, Ukraine, destroyed in 1936 by the Soviets. The Cathedral is known as a burial site of Hetman Petro Sahaidachnyi (1582-1622), a Ukrainian Cossack political and civic leader, guardian and patron of Kyiv Brotherhood Monastery. The collection of archival documents, blueprints, stock sources, photographs and cartographic materials of the 17th-19th centuries, as well as historical works of the 19th-21st centuries, were thoroughly analyzed and used as a basis for the interpretation of geophysical results. The set of historical data covers the period from 1615, that is, from the beginning of the construction of the wooden Epiphany Church, the predecessor of the cathedral, to the present day. Summarized information about the construction, restoration after the fire, functioning and destruction of the Cathedral, as well as about the construction on its site in the 20th century, archaeological research in the 20th-21st centuries, was used to clarify possible location of hidden foundations and target GPR measurements. In this context, written testimonies of archaeologists who personally observed the foundations of the cathedral became especially valuable. The shielded GPR antennas, with a central frequency of 300 MHz and 700 MHz, is used for non-invasive investigation. GPR scanning specified the site and showed the best-preserved group of foundations of the western wall of the Epiphany Cathedral. An integrated historical and geophysical study provided the basis to certify the foundation of the Epiphany Cathedral as a protected object of cultural heritage and facilitated its archaeological research.

Development and application of air-coupled GPR antenna for coal-rock interface detection in mines

Rapid and high precision detection of coal-rock interface is one of the key problems in intelligent coal mining. In this paper, an air-coupled ground penetrating radar (GPR) antenna is developed for coal-rock interface detection in fully mechanized mining face, which can realize non-contacting, high precision, high-resolution detection during the mining process. The main features of the air-coupled GPR antenna are ultra-wideband, narrow beam, and high gain. For the vertical polarization pattern at 1200 MHz, the main lobe amplitude is 6.29dBi and the main lobe width (3 dB) is 108.3°, as well as the relative bandwidth of the antenna can reach 51%. For design validations, the novel air-coupled GPR antenna developed in this paper is tested in mine, the detection error and the average error percentage are ±1.2 cm and 8.6%, respectively, which shows the superior detection ability of the novel air-coupled GPR antenna and the applicability in complex geological environment, providing technical support for the identification of coal-rock interface in intelligent coal mining.

Developing a realistic numerical equivalent of a GPR antenna transducer using global optimizers

AbstractNumerical modelling of a ground‐penetrating radar (GPR) has been widely used for predicting and assessing its performance. As the transmitter and the receiver are the most essential components of a GPR system, an accurate representation of them should be included in a model. Simulating a real system is particularly challenging, especially when it comes to commercial GPR systems. A three‐dimensional model based on a 2000 MHz ‘palm’ antenna from Geophysical Survey Systems, Inc. (GSSI) is presented in this paper. The geometric features of the transducers were modelled via visual inspection, whereas their unknown dielectric properties were estimated using global optimizers in order to minimize the differences between real and synthetic measurements. In particular, the antenna was calibrated in free space and on top of a metal plate. Subsequently, the resulting model was successfully tested in various case studies to assess its performance. Models of two units of the same transducer were developed, showing that units of the same system in general are not identical. The results support the premise that global optimizers can be used to provide information on key aspects of the dielectric structure of the transducer and allow us to accurately model its behaviour in various environments.

A Compact Low Frequency Vivaldi Antenna for Ground Penetrating Radar

A Compact Low Frequency Vivaldi Antenna for Ground Penetrating Radar

An Approach for Easy Detection of Buried FRP Composite/Non-Metallic Pipes Using Ground-Penetrating Radar.

Pipelines remain the safest means of transporting natural gas and petroleum products. Nonetheless, the pipeline infrastructure in the US is facing major challenges, especially in terms of corrosion of steel/metallic pipes and excavation damage of onshore pipelines (leading to oil spills, explosions, and deaths). Corrosion of metallic pipelines can be avoided by using non-corrosive materials such as plastic pipes for low-pressure applications and glass-fiber-reinforced polymer (GFRP) composite pipes for transporting high-pressure oil and natural gas. However, buried non-metallic pipelines are not easily detectable, which can lead to increased excavation damage during construction and rehabilitation work. Alternative strategies for making buried non-metallic pipes easily locatable using ground-penetrating radar (GPR) were investigated in this study. Results from this study have shown that using carbon fabric or an aluminum foil overlay on non-metallic pipes before burying in soil significantly increases the reflected GPR signal amplitude, thereby making it easier to locate such pipelines. The reflected GPR signal amplitude for pipe sections with carbon fabric or aluminum foil overlays was found to have increased by a factor of up to 4.5 over the control samples. The results also highlight the importance of selecting the appropriate antenna frequency for GPR surveys, since wet silt loam soil and clay significantly reduce the penetration depths of the radar signals produced by the GPR antennae.

Processing GPR Surveys in Civil Engineering to Locate Buried Structures in Highly Conductive Subsoils

Many studies have illustrated the great benefit of ground-penetrating radar (GPR) in civil engineering. However, in some cases, this geophysical survey method does not produce the desired results due to the electromagnetic characteristics of the subsoil. This study presents the results obtained in two locations near Linares (southern Spain), evaluating the detection of structures buried in conductive host materials (0.02 S/m in site 1 and 0.015 S/m in site 2) characterized by strong signal attenuation. Accounting for the study depth, which was 1.5 m, a 500 MHz shielded GPR antenna was used at both sites. At the first site, a controlled experiment was planned, and it consisted of burying three linear elements. An iron pipe, a PVC pipe, and a series of precast blocks were buried at a depth of 0.5 m in a subsoil composed of highly conductive clayey facies. To eliminate additional multiples caused by other superficial structures and increasing the high-frequency content, the predictive deconvolution flow was applied. In the 3D processing, the cover surfaces technique was used. Once the acquired GPR signals was analyzed and the optimal processing flow established, a second site in which different infrastructures in a conductive host medium formed by marly facies was explored. The 2D flow and 3D processing applied in this work allows to detect and see the continuity of some structures not visible for the default processing.

Wavelet Analysis of GPR Data for Belowground Mass Assessment of Sorghum Hybrid for Soil Carbon Sequestration

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical.

ДВУХПОЗИЦИОННЫЙ ГЕОРАДИОЛОКАТОР НА БПЛА ДЛЯ ОПЕРАТИВНОГО МОНИТОРИНГА ПОДПОВЕРХНОСТНЫХ ВОДНЫХ РЕСУРСОВ

This paper considers the implementation of a two-position ground penetrating radar (GPR) located on board unmanned aerial vehicles (UAVs) and designed for the operational search for groundwater in arid and desert regions of Mongolia, which is an extremely urgent task. With the help of synthesizing the GPR antenna aperture on the UAV, a significant increase in information content is achieved compared to existing GPRs, since it becomes possible to obtain a panoramic radar image of the subsurface layers of the probed medium with the required resolution with a sufficiently wide swath. On the other hand, the necessary efficiency (required, for example, when monitoring vast desert areas) can only be achieved with a sufficiently wide field of view created on the basis of the two-position sounding principle, where the emitting and receiving signals are formed and processed on UAVs of different or equal heights flying with the same speed and spaced orthogonally to the direction of their movement, which makes it possible to use quasi-mirror reflection. In this case, the swath will be noticeably larger than in the case of nadir sounding. In a condensed form, the article considers the following issues: the model of a subsurface aquifer, the geometry of a two-position GPR in the quasi-mirror sounding mode, up to the features of synthesizing the aperture (Doppler narrowing of the radiation pattern) of the receiving antenna, resolution, the required potential of a two-position GPR for reliable detection of an aquifer subsurface horizon, accuracy measurements of its depth, as well as the need for experimental work to test the theoretical provisions.